Ecological processes depend on …

I have been known to say (ok – I say it all the time) that ecologists should never equivocate when speaking to the public. Whether it’s in a media release, blog post, television presentation or newspaper article, just stick to ‘yes’ or ‘no’. In other words, don’t qualify your answer with some horrid statistical statement (i.e., in 95% of cases …) or say something like “… but it really depends on …”. People don’t understand uncertainty – to most people, ‘uncertainty’ means “I don’t know” or worse, “I made it all up”.

But that’s only in the movies.

In real ‘ecological’ life, things are vastly different. It’s never as straightforward as ‘yes’ or ‘no’, because ecology is complex. There are times that I forget this important aspect when testing a new hypothesis with what seem like unequivocal data, but then reality always hits.

Our latest paper is the epitome of this emergent complexity from what started out as a fairly simple question using some amazing data. What makes birds change their range1? We looked at this question from a slightly different angle than had been done before because we had access to climate data, life-history data and most importantly, actual range change data. It’s that latter titbit that is typically missing from studies aiming to understand what drives species toward a particular fate; whether it’s a species distribution model predicting the future habitat suitability of some species as a function of climate change, or the past dynamics of some species related to its life history pace, most often the combined dynamics are missing.

Our first challenge was how to model the direction of range change within the same framework – some species had contracted their range between the 1970s and 1990s, while others had expanded. The second challenge was accounting for phylogenetic non-independence (a staple assumption of linear models is that the underlying data are independent – evolution makes that impossible for individual species). Our third challenge was to find a way of characterising the configuration of a species’ range (i.e., degree of fragmentation) using a metric uncorrelated to total range size. Finally, we needed to account for non-linearities in response to climatic conditions like minimum winter temperature and annual precipitation. Phew!

Let’s try to run through the major discoveries without too much focus on that niggling ‘… but it depends on …” qualification.

Interestingly, many species that had contracted their range were not locally threatened, nor were species that expanded their range universally considered safe.

Also, the direction of change mattered less than the magnitude of change. In other words, our predictors generally had a similar effect regardless of the direction of the change.

Climate (not weather) came out as the most important predictor of range dynamics, but the relative influence of each driver depended on where the species spends most of its time: northern species changed their range more in warmer and drier regions, whereas southern species changed more in colder and wetter environments.

Species with slow life history (larger body size) tended to change their range more than species with faster life histories, whereas species with greater natal dispersal capacity resisted contraction and, counterintuitively, expansion.

Counter-intuitively, we found that higher geographical fragmentation of a species’ range also increased expansion probability, possibly indicating a release from a previously limiting condition.

Well, that’s a bit to get one’s head around. I think the generalisable statement one can make from all this is that it’s all about context, context, context. Just when you expect a species to behave in a particular way to an environmental pressure, it might just surprise you that it does exactly the opposite.

We also have a few more caveats about generalisability (don’t we all):

Britain has been extensively modified for centuries – we’re probably dealing with a fairly perturbation-resistant remnant avian biota, so the results might not apply to areas having recently undergone habitat change.

Britain is also not the only place these species hang out – migratory species likely respond differently than sedentary ones (indeed, most of the world’s bird species are sedentary).

Britain, like much of Europe and eastern USA, has had quite a lot of forest regrowth following agricultural abandonment over the last few decades. This likely affects some species differently than others.

Hopefully all this makes sense to you. I feel we’ve probably raised more questions than we’ve answered, but isn’t that what the pursuit of knowledge is all about? To me the main take-home message is that simple species distribution models predicting range change based on some future climate probably don’t capture all the nuances of what makes a species inhabit the range it currently does.

5 responses

[…] As you might already know, the Great Britons are a little cuckoo for birds — I’d even wager that the country produces more twitchers than any other country on Earth. The plus side is that there are few national taxa better censused and studied that British birds, because so many non-scientists get into the spirit of data collection. Hell, I’ve even had a play with some of their datasets. […]

[…] taxonomic and temporal breadth to summarise these components of complex ecosystems (i.e., ecology is complex). It’s no real surprise, and even though we should put a lot more emphasis on targeted, […]

I have trouble with the concept of hypothesis testing having anything to do with what scientists actually do. All of your post is about analysing data and examining the strength of different effects in very complicated systems. Hypothesis testing seems a far too simplistic concept. I looked for examples in your post and I could not find any. Indeed, I have been looking since 1983 when I first read “What is this thing called science?” by A.F. Chalmers. I think we need a more accurate portrayal of science for the community to understand the process.

On the contrary, Robert – the paper was full of hypotheses, but multiple working hypotheses (not the bog-standard, unrealistic Neyman-Pearson hypothesis testing framework). I didn’t exactly put them down as such in the post, and admittedly, they evolved as the statistical analysis did, but it is still very much a hypothesis-driven analysis. I suggest you read the paper.